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    Reconstruction of Mesoscale Precipitation Fields from Sparse Observations in Complex Terrain

    Source: Journal of Climate:;2001:;volume( 014 ):;issue: 015::page 3289
    Author:
    Schmidli, Jürg
    ,
    Frei, Christoph
    ,
    Schär, Christoph
    DOI: 10.1175/1520-0442(2001)014<3289:ROMPFF>2.0.CO;2
    Publisher: American Meteorological Society
    Abstract: The feasibility of a statistical reconstruction of mesoscale precipitation fields over complex topography from a sparse rain gauge network is examined. Reconstructions of gridded monthly precipitation for the European Alps (resolution 25 km, 1202 grid points) are derived from rain gauge samples (70?200-km interstation distance, 25?150 stations). The statistical model is calibrated over a 15-yr period, and the reconstructed fields are evaluated for the remaining 5 yr of the period 1971?90. The experiments are used to define the statistical setup, to assess the data requirements, and to describe the error statistics of a centennial reconstruction to be used in a forthcoming study. Reduced-space optimal interpolation is employed as the reconstruction method, involving data reduction by empirical orthogonal functions (EOFs) and least squares optimal estimation of EOF coefficients. Also, a procedure to define covariance-guided station samples with a ?representative? spatial distribution for the reconstruction is proposed. Using a covariance-guided reference sample of 53 stations, the reconstruction accounts for 77% of the total variance. For individual grid points the relative reconstruction error (error variance divided by data variance) varies between 10% and 40%; this value drops to 2%?10% when considering subdomain means of 100 ? 100 km2. The mesoscale patterns of the fields and multiyear precipitation anomalies are accurately reproduced. The EOF truncation is identified as the major limitation of the reconstruction skill but is necessary to avoid overfitting. Reconstructions from covariance-guided representative samples exhibit superior skill in comparison with those from randomly distributed stations. The skill of the reconstruction was found to depend marginally on the choice of the calibration period within the 20 yr, even when months with exclusively positive or negative values of the North Atlantic oscillation index were selected for calibration. This result indicates that the reconstruction model provides appreciable temporal stationarity.
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      Reconstruction of Mesoscale Precipitation Fields from Sparse Observations in Complex Terrain

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4199022
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    • Journal of Climate

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    contributor authorSchmidli, Jürg
    contributor authorFrei, Christoph
    contributor authorSchär, Christoph
    date accessioned2017-06-09T16:00:19Z
    date available2017-06-09T16:00:19Z
    date copyright2001/08/01
    date issued2001
    identifier issn0894-8755
    identifier otherams-5856.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4199022
    description abstractThe feasibility of a statistical reconstruction of mesoscale precipitation fields over complex topography from a sparse rain gauge network is examined. Reconstructions of gridded monthly precipitation for the European Alps (resolution 25 km, 1202 grid points) are derived from rain gauge samples (70?200-km interstation distance, 25?150 stations). The statistical model is calibrated over a 15-yr period, and the reconstructed fields are evaluated for the remaining 5 yr of the period 1971?90. The experiments are used to define the statistical setup, to assess the data requirements, and to describe the error statistics of a centennial reconstruction to be used in a forthcoming study. Reduced-space optimal interpolation is employed as the reconstruction method, involving data reduction by empirical orthogonal functions (EOFs) and least squares optimal estimation of EOF coefficients. Also, a procedure to define covariance-guided station samples with a ?representative? spatial distribution for the reconstruction is proposed. Using a covariance-guided reference sample of 53 stations, the reconstruction accounts for 77% of the total variance. For individual grid points the relative reconstruction error (error variance divided by data variance) varies between 10% and 40%; this value drops to 2%?10% when considering subdomain means of 100 ? 100 km2. The mesoscale patterns of the fields and multiyear precipitation anomalies are accurately reproduced. The EOF truncation is identified as the major limitation of the reconstruction skill but is necessary to avoid overfitting. Reconstructions from covariance-guided representative samples exhibit superior skill in comparison with those from randomly distributed stations. The skill of the reconstruction was found to depend marginally on the choice of the calibration period within the 20 yr, even when months with exclusively positive or negative values of the North Atlantic oscillation index were selected for calibration. This result indicates that the reconstruction model provides appreciable temporal stationarity.
    publisherAmerican Meteorological Society
    titleReconstruction of Mesoscale Precipitation Fields from Sparse Observations in Complex Terrain
    typeJournal Paper
    journal volume14
    journal issue15
    journal titleJournal of Climate
    identifier doi10.1175/1520-0442(2001)014<3289:ROMPFF>2.0.CO;2
    journal fristpage3289
    journal lastpage3306
    treeJournal of Climate:;2001:;volume( 014 ):;issue: 015
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian